Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network.

ABSTRACT: The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. T

SUBMITTER: Wang Z 

PROVIDER: S-EPMC8516202 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature